examples/igorio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IgorProIO Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import neo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IBW support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aG3XeEQP7sq+mnrFDB7D4jssBuGnOYt7Zspebpk/mvk+fC8Csa1o27u8XlQDw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\nEZGY/j+DnKE7AWKkdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108dd4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Downloaded from Human Brain Project Collaboratory\n",
    "# Digital Reconstruction of Neocortical Microcircuitry (nmc-portal)\n",
    "# http://microcircuits.epfl.ch/#/animal/8ecde7d1-b2d2-11e4-b949-6003088da632\n",
    "x = neo.io.IgorIO(filename='nmc-portal/grouped_ephys/B95/B95_Ch0_IDRest_107.ibw')\n",
    "signal = x.read_analogsignal()\n",
    "plt.plot(signal.times,signal)\n",
    "plt.xlabel(signal.sampling_period.dimensionality)\n",
    "plt.ylabel(signal.dimensionality);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### PXP support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aG3XeEQP7sq+mnrFDB7D4jssBuGnOYt7Zspebpk/mvk+fC8Csa1o27u8XlQDw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\nEZGY/j+DnKE7AWKkdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d79da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same as above, but the .ibw file was loaded into Igor and saved as part of a .pxp file.  \n",
    "x = neo.io.IgorIO(filename='nmc-portal/grouped_ephys/B95/B95_Ch0_IDRest_107.pxp')\n",
    "signal = x.read_analogsignal(path='root:AA_IDRest_Ch0_107')\n",
    "plt.plot(signal.times,signal)\n",
    "plt.xlabel(signal.sampling_period.dimensionality)\n",
    "plt.ylabel(signal.dimensionality);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}